NEW YORK (GenomeWeb) – Researchers at Pacific Northwest National Laboratory have developed a mass spec method that could enable discovery proteomics experiments with the sensitivity of targeted assays.
Named discovery and targeted monitoring mass spec, or DTM, the method uses ion mobility mass spec combined with the addition of heavy-labeled peptides to simultaneously achieve the breadth of a discovery experiment and the depth of a selected reaction-monitoring-style targeted assay.
Detailed in a paper published last month in Molecular and Cellular Proteomics, the approach could also allow scientists to speed assay development and improve throughput, as well as work with smaller sample sizes, Erin Baker, a PNNL researcher and author on the study told GenomeWeb.
One of the major advantages of mass spec as a proteomics discovery tool is its breadth. While antibody-based approaches are typically limited to the range of hundreds of protein measurements per experiment, mass spec runs can identify and quantify upwards of 10,000 proteins in a single run, making it the preferred tool for generating broad proteome profiles.
Discovery experiments are limited in sensitivity, however, and can struggle to measure low-abundance proteins. To address this problem, researchers typically use targeted methods like SRM, which measure a smaller number of proteins (typically in the range of hundreds or fewer) with higher sensitivity and reproducibility than a discovery experiment.
In recent years, the rise of data independent acquisition (DIA) mass spec methods has blurred the lines between discovery and targeted experiments. DIA mass spec is technically a targeted method, using a data analysis method similar to that of SRM to quantify peptides and proteins of interest. At the same time, DIA assays are able to measure on the order of 5,000 to 6,000 proteins, giving them a breadth similar to that of a discovery experiment.
DIA assays are not as sensitive as SRM assays, however, and, while they can measure thousands of proteins in a run, they are generally not as comprehensive as a standard shotgun experiment.
The DTM method could allow for similar breadth but at SRM-levels of sensitivity, Baker said.
The method uses the accurate mass and time tagging (AMT) approach, in which researchers first run a shotgun experiment with their sample of interest to create a library linking peptides to their specific LC elution times and accurate mass information. In the case of DTM, the researchers also include the peptides' ion mobility drift times, which provides them three dimensions of data, as opposed to two, to search against, improving the accuracy of peptide identifications.
They then run their samples and search them against the established ATM library. They also spike in heavy-labeled version of peptides of particular interest. Inclusion of the labeled peptides allows for more confident identification and precise quantitation of these analytes, essentially enabling researchers to measure them at SRM-level limits of detection while simultaneously conducting a larger discovery experiment.
The ability to make targeted measurements within a larger discovery experiment has a number of advantages, Baker noted. Running both experiments at once improves throughput. It also reduces the amount of sample required, often an important factor, she noted, when dealing with small clinical samples.
The DTM approach is also easier in terms of assay development, compared to standard SRM methods, Baker said.
"With SRM you have to do a lot of upfront work to figure out which transition is the best to follow," she said. DTM "is much easier because we just took heavylabeled peptides matching the specific peptides we were looking for and spiked them in."
Importantly, she noted, performance did not appear to suffer. In the MCP paper, the researchers used the DTM method to analyze patient-derived xenograft mouse breast cancer tissue, targeting 20 specific peptides while also profiling the entire proteome. DTM was able to quantify all 20 peptides while SRM quantified 19 with good agreement between the two methods.
On the discovery side, the researchers compared the DTM method to the standard shotgun analysis on a Thermo Fisher Scientific Q Exactive instrument, finding that 9,832 peptides were detected by both platforms, 2,511 peptides were detected only by the Q Exactive, and 8,055 peptides were detected only by the DTM approach, which Baker and her colleagues ran on an Agilent 6538 QTOF coupled to a 1-meter IMS drift cell developed by PNNL researchers. (Agilent uses a version of that IMS cell in its IMS instruments, Baker noted.) She said that while the MCP work used Agilent instruments, the method should work on a variety of IMS-QTOF systems.
"So when we compared [DTM] to each approach individually, we were actually doing just as well or better," Baker said. "We were detecting more peptides than we were seeing with the Q Exactive with MS/MS, and then when we compared it to SRM we were also seeing the same peptides as SRM with similar quantitation."
Key to the method is use of the IMS technology, she noted. "It really spreads everything out and gives you extra separation and more spacing of the ions so you don't have all the ions hitting the detector at the same time."
Baker called the MCP paper a proof of principle, saying that what the researchers now aimed to do was significantly up the number of heavy-labeled peptides they spiked into their experiments to enable SRM-level quantitation of large numbers of proteins.
She said that she didn't expect adding more labeled peptides would hamper the assay's performance, noting that, given the complexity of the proteome, "adding an extra thousand or even ten thousand heavy-labeled" only increases sample complexity by a small proportion.
Baker said she and her colleagues were planning to start this next stage of experiments by spiking in around 5,000 labeled peptides, which, assuming the results from the MCP paper hold, would represent one of the broadest proteomic analyses to date to achieve such a level of sensitivity.